Traditional NR-SfM tackles the task of dynamic 3D inference by fitting parametric 3D morphable models [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, or
fusing non-parametric depth scans of generic dynamic scenes [9]}, [10]}, [11]}, [12]}, [13]}.
All of these approaches aim to recover accurate surface geometry at each time step and their performance is measured with ground truth 3D geometry or 2D correspondences with PCK [14]} when such ground truth is not available.
In this paper, we analyze recent dynamic view synthesis methods whose goal is to generate a photo-realistic novel view.
Due to their goal, these methods do not focus on evaluation against ground truth 3D geometry, but we take inspiration from prior NR-SfM works to evaluate the quality of the inferred 3D dynamic representation based on correspondences.
We also draw inspiration from previous NR-SfM work that analyzed camera/object speed and 3D reconstruction quality [15]}, [16]}.
